/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin colpack_jac.cpp$$
$spell
	colpack_jac
	jacobian
$$

$section ColPack: Sparse Jacobian Example and Test$$


$code
$srcfile%example/sparse/colpack_jac.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++

# include <cppad/cppad.hpp>
bool colpack_jac(void)
{	bool ok = true;
	using CppAD::AD;
	using CppAD::NearEqual;
	typedef CPPAD_TESTVECTOR(AD<double>)            a_vector;
	typedef CPPAD_TESTVECTOR(double)                d_vector;
	typedef CppAD::vector<size_t>                   i_vector;
	typedef CppAD::sparse_rc<i_vector>              sparsity;
	typedef CppAD::sparse_rcv<i_vector, d_vector>   sparse_matrix;

	// domain space vector
	size_t n = 4;
	a_vector  a_x(n);
	for(size_t j = 0; j < n; j++)
		a_x[j] = AD<double> (0);

	// declare independent variables and starting recording
	CppAD::Independent(a_x);

	size_t m = 3;
	a_vector  a_y(m);
	a_y[0] = a_x[0] + a_x[1];
	a_y[1] = a_x[2] + a_x[3];
	a_y[2] = a_x[0] + a_x[1] + a_x[2] + a_x[3] * a_x[3] / 2.;

	// create f: x -> y and stop tape recording
	CppAD::ADFun<double> f(a_x, a_y);

	// new value for the independent variable vector
	d_vector x(n);
	for(size_t j = 0; j < n; j++)
		x[j] = double(j);

	/*
	      [ 1 1 0 0  ]
	jac = [ 0 0 1 1  ]
	      [ 1 1 1 x_3]
	*/
	// Normally one would use CppAD to compute sparsity pattern, but for this
	// example we set it directly
	size_t nr  = m;
	size_t nc  = n;
	size_t nnz = 8;
	sparsity pattern(nr, nc, nnz);
	d_vector check(nnz);
	for(size_t k = 0; k < nnz; k++)
	{	size_t r, c;
		if( k < 2 )
		{	r = 0;
			c = k;
		}
		else if( k < 4 )
		{	r = 1;
			c = k;
		}
		else
		{	r = 2;
			c = k - 4;
		}
		pattern.set(k, r, c);
		if( k == nnz - 1 )
			check[k] = x[3];
		else
			check[k] = 1.0;
	}

	// using row and column indices to compute non-zero in rows 1 and 2
	sparse_matrix subset( pattern );

	// check results for both CppAD and Colpack
	for(size_t i_method = 0; i_method < 4; i_method++)
	{	// coloring method
		std::string coloring;
		if( i_method % 2 == 0 )
			coloring = "cppad";
		else
			coloring = "colpack";
		//
		CppAD::sparse_jac_work work;
		size_t group_max = 1;
		if( i_method / 2 == 0 )
		{	size_t n_sweep = f.sparse_jac_for(
				group_max, x, subset, pattern, coloring, work
			);
			ok &= n_sweep == 4;
		}
		else
		{	size_t n_sweep = f.sparse_jac_rev(
				x, subset, pattern, coloring, work
			);
			ok &= n_sweep == 2;
		}
		const d_vector& hes( subset.val() );
		for(size_t k = 0; k < nnz; k++)
			ok &= check[k] == hes[k];
	}
	return ok;
}
// END C++
